Investigating probabilistic forecasting of tropical cyclogenesis over the North Atlantic using linear and non-linear classifiers [electronic resource] /
Abstract (Summary)Abstract: Current numerical weather prediction models experience great difficulty in forecasting tropical cyclogenesis, primarily because of limitations of cloud parameterizations and observations. This research was performed with the aim of filling the void of objective guidance for tropical cyclogenesis. A new dataset of cloud clusters is created through the examination of infrared (IR) satellite imagery over the tropical Atlantic during the 1998-2001 hurricane seasons. Eight large-scale predictors of tropical cyclogenesis were then calculated from NCEP-NCAR Reanalysis dataset for each 6-hour interval of the cloud cluster life cycle extending back to 48 hours prior to genesis. Independent classifications were then performed on the entire dataset using both discriminant analysis (DA) and an artificial neural network (NN). The performance of each classifier was investigated through statistical scores and a series of case studies from the 1998-2001 Atlantic hurricane seasons. The new cloud cluster database reflects that. 432 cloud clusters, of which 62 developed into tropical depressions, were tracked during the four seasons. Independent DA classifications show forecast skill over climatology. For the "prime" development season of August - October, the DA correctly forecast a higher percentage of clusters than climatology for all forecast periods. The most important predictors are latitude and the vertical shear structure. The NN generally performed better with non-developing cloud clusters; however, there are indications that the NN suffers from over fitting to a greater degree than DA. The DA appears to generalize much better than the NN in most cases. The large-scale predictors over-forecast genesis in a favorable shear environment, even if the thermodynamic environment is marginal. Also, the lack of any information on the convective structure of the cloud cluster will decrease forecast accuracy in some cases. Results suggest that this model has sufficient potential to be implemented as an objective forecast tool. Each predictor can easily be calculated from an analysis field that is routinely available to forecasters. The inclusion of mesoscale predictors, especially satellite derived temperature, moisture, and wind data, is thought to be an important next step for improvement of forecasts.
School:The Ohio State University
School Location:USA - Ohio
Source Type:Master's Thesis
Date of Publication: